Plot the value function against the state variables. What is the intuition?
Plot the policy function against the state variables. What is the intuition?
How do the two plots above change with the values of \(\alpha\) and \(\delta\)?
Write a subroutine that takes the model’s solution and parameters as inputs, then produces a simulated data set as output.
Use the Tauchen (1986) method to discretize the state space.
Day 2
Which features of the data will you ask the model to fit? These features may include means, variances, regression coefficients, or correlations.
Explain which moment or moments are most important for identifying each model parameter. Create a table showing how each simulated moment changes as you perturb \(\alpha\) and \(\delta\).
Downlad the data and read the documentation. The data set contains cleaned Compustat data on non-financial firms from 1971 - 2016. These variables should give you a hint about the types of moments that will work well.
Write a subroutine for computing the vector of moments from the actual data and simulated data.
If one of your moments is an AR1 coefficient, use the Han and Philips method at the end of Toni’s slides from today.
Day 3
Use influence functions to estimate the covariance matrix for the moments you have chosen, clustering by firm.
Compute the optimal SMM weight matrix.
Code up the SMM routine.
Write the code for finding the parameter vector that minimizes the econometric score, i.e. the distance between the actual and simulated moments.
Choose a minimizer that will avoid local minima, such as the simulated annealing minimization algorithm or particle swarm optimization.
Day 4
SMM estimator
Compute your final parameter estimates and their standard errors. Do they make sense?
How well does the model fit the data? Compare the simulated and empirical moments.
Does the model fail the test of overidnentifying restrictions?
What would be an interesting counterfactual experiment to run? How would you do it?
Source Code
---title: Project Overview---### Day 1- Plot the value function against the state variables. What is the intuition? - Plot the policy function against the state variables. What is the intuition?- How do the two plots above change with the values of $\alpha$ and $\delta$?- Write a subroutine that takes the model's solution and parameters as inputs, then produces a simulated data set as output. - Use the Tauchen (1986) method to discretize the state space.### Day 2- Which features of the data will you ask the model to fit? These features may include means, variances, regression coefficients, or correlations.- Explain which moment or moments are most important for identifying each model parameter. Create a table showing how each simulated moment changes as you perturb $\alpha$ and $\delta$.- Downlad the data and read the documentation. The data set contains cleaned Compustat data on non-financial firms from 1971 - 2016. These variables should give you a hint about the types of moments that will work well.- Write a subroutine for computing the vector of moments from the actual data and simulated data. - If one of your moments is an AR1 coefficient, use the Han and Philips method at the end of Toni's slides from today. ### Day 3- Use influence functions to estimate the covariance matrix for the moments you have chosen, clustering by firm.- Compute the optimal SMM weight matrix. - Code up the SMM routine. - Write the code for finding the parameter vector that minimizes the econometric score, i.e. the distance between the actual and simulated moments. - Choose a minimizer that will avoid local minima, such as the simulated annealing minimization algorithm or particle swarm optimization. ### Day 4- SMM estimator- Compute your final parameter estimates and their standard errors. Do they make sense?- How well does the model fit the data? Compare the simulated and empirical moments. - Does the model fail the test of overidnentifying restrictions?- What would be an interesting counterfactual experiment to run? How would you do it?